Multi-Criteria Kinematic Optimization of a Front Multi-Link Suspension Mechanism Using DOE Screening and Regression Model

Article Preview

Abstract:

This paper approaches the multi-criteria kinematic optimization of a front multi-link suspension mechanism. The optimization purpose is to minimize the variations of the wheel track, wheelbase, castor angle, and induced deflection angle, the monitored values being the root mean squares during simulation. The locations of the joints by which the guiding links/arms are connected to the adjacent parts are used as independent variables in the optimization process. The investigation strategy is based on a design of experiments technique - DOE Screening, obtaining the appropriate regression model. The goodness-of-fit has been verified by computing the variance in the predicted results versus the real data, the probability that the fitted model has no useful terms, and the significance of the regression. The study is performed by using the multi-body system environment ADAMS of MSC Software.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

351-356

Citation:

Online since:

July 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] E.J. Haug, K.K. Choi, Virtual prototyping simulation for design of mechanical systems, Transaction of ASME, n. 117, pp.63-70, 1995.

Google Scholar

[2] W.O. Schiehlen, Multibody systems dynamics: roots & perspectives, Multibody Systems Dynamics, vol. 1 (2), pp.149-188, 1997.

DOI: 10.1023/a:1009745432698

Google Scholar

[3] S. Staicu, Dynamics analysis of the Star parallel manipulator, Robotics and Autonomous Systems, vol. 57 (11), pp.1057-1064, 2009.

DOI: 10.1016/j.robot.2009.07.005

Google Scholar

[4] S. Staicu, Dynamics of the 6-6 Stewart parallel manipulator, Robotics and Computer-Integrated Manufacturing, vol. 27 (1), pp.212-220, 2011.

DOI: 10.1016/j.rcim.2010.07.011

Google Scholar

[5] C. Alexandru, Software platform for analyzing and optimizing the mechanical systems, Proceedings of the 10th SYROM Symposium, Springer, pp.665-677, 2009.

DOI: 10.1007/978-90-481-3522-6_56

Google Scholar

[6] R. Grossman, R. Del Vecchio, Design of experiments, John Wiley & Sons, 2007.

Google Scholar

[7] R.G. Orwin, D.S. Cordray, Effects of deficient reporting on meta-analysis: a conceptual framework and reanalysis, Psychologica, vol. 97 (1), pp.134-147, 1985.

DOI: 10.1037/0033-2909.97.1.134

Google Scholar